import sys
import typing
from collections.abc import Hashable
from typing import Union, Any, Type, List
from opendp.mod import UnknownTypeException
from opendp._lib import ATOM_EQUIVALENCE_CLASSES
if sys.version_info >= (3, 7):
from typing import _GenericAlias
else:
from typing import GenericMeta as _GenericAlias
ELEMENTARY_TYPES = {int: 'i32', float: 'f64', str: 'String', bool: 'bool'}
# all ways of providing type information
RuntimeTypeDescriptor = Union[
"RuntimeType", # as the normalized type -- SubstituteDistance; RuntimeType.parse("i32")
_GenericAlias, # a python type hint from the std typing module -- List[int]
str, # plaintext string in terms of rust types -- "Vec<i32>"
Type[Union[typing.List, typing.Tuple, int, float, str, bool]], # using the python type class itself -- int, float
tuple, # shorthand for tuples -- (float, "f64"); (SubstituteDistance, List[int])
]
[docs]
class RuntimeType(object):
"""Utility for validating, manipulating, inferring and parsing/normalizing type information.
"""
def __init__(self, origin, args=None):
if not isinstance(origin, str):
raise ValueError("origin must be a string", origin)
self.origin = origin
self.args = args
def __eq__(self, other):
if isinstance(other, str):
other = RuntimeType.parse(other)
return self.origin == other.origin and self.args == other.args
def __str__(self):
result = self.origin or ''
if result == 'Tuple':
return f'({", ".join(map(str, self.args))})'
if self.args:
result += f'<{", ".join(map(str, self.args))}>'
return result
[docs]
@classmethod
def parse(cls, type_name: RuntimeTypeDescriptor, generics: List[str] = None) -> Union["RuntimeType", str]:
"""Parse type descriptor into a normalized rust type.
Type descriptor may be expressed as:
- python type hints from std typing module
- plaintext rust type strings for setting specific bit depth
- python type class - one of {int, str, float, bool}
- tuple of type information - for example: (float, float)
:param type_name: type specifier
:param generics: For internal use. List of type names to consider generic when parsing.
:type: List[str]
:return: Normalized type. If the type has subtypes, returns a RuntimeType, else a str.
:rtype: Union["RuntimeType", str]
:raises UnknownTypeError: if `type_name` fails to parse
:examples:
>>> from opendp.typing import RuntimeType, L1Distance
>>> assert RuntimeType.parse(int) == "i32"
>>> assert RuntimeType.parse("i32") == "i32"
>>> assert RuntimeType.parse(L1Distance[int]) == "L1Distance<i32>"
>>> assert RuntimeType.parse(L1Distance["f32"]) == "L1Distance<f32>"
"""
generics = generics or []
if isinstance(type_name, RuntimeType):
return type_name
# parse type hints from the typing module
if isinstance(type_name, _GenericAlias):
if sys.version_info < (3, 8):
raise NotImplementedError("parsing type hint annotations are only supported in python 3.8 and above")
origin = typing.get_origin(type_name)
args = [RuntimeType.parse(v, generics=generics) for v in typing.get_args(type_name)] or None
if origin == tuple:
origin = 'Tuple'
if origin == list:
origin = 'Vec'
return RuntimeType(RuntimeType.parse(origin, generics=generics), args)
# parse a tuple of types-- (int, "f64"); (List[int], (int, bool))
if isinstance(type_name, tuple):
return RuntimeType('Tuple', list(cls.parse(v, generics=generics) for v in type_name))
# parse a string-- "Vec<f32>",
if isinstance(type_name, str):
type_name = type_name.strip()
if type_name in generics:
return GenericType(type_name)
if type_name.startswith('(') and type_name.endswith(')'):
return RuntimeType('Tuple', cls._parse_args(type_name[1:-1], generics=generics))
start, end = type_name.find('<'), type_name.rfind('>')
# attempt to upgrade strings to the metric/measure instance
origin = type_name[:start] if 0 < start else type_name
closeness = {
'SubstituteDistance': SubstituteDistance,
'SymmetricDistance': SymmetricDistance,
'AbsoluteDistance': AbsoluteDistance,
'L1Distance': L1Distance,
'L2Distance': L2Distance,
'MaxDivergence': MaxDivergence,
'SmoothedMaxDivergence': SmoothedMaxDivergence
}.get(origin)
if closeness is not None:
if isinstance(closeness, (SensitivityMetric, PrivacyMeasure)):
return closeness[cls._parse_args(type_name[start + 1: end], generics=generics)[0]]
return closeness
domain = {
'AllDomain': AllDomain,
'BoundedDomain': BoundedDomain,
'VectorDomain': VectorDomain,
'OptionNullDomain': OptionNullDomain,
'InherentNullDomain': InherentNullDomain,
'SizedDomain': SizedDomain
}.get(origin)
if domain is not None:
return domain[cls._parse_args(type_name[start + 1: end], generics=generics)[0]]
if 0 < start < end < len(type_name):
return RuntimeType(origin, args=cls._parse_args(type_name[start + 1: end], generics=generics))
if start == end < 0:
return type_name
if isinstance(type_name, Hashable) and type_name in ELEMENTARY_TYPES:
return ELEMENTARY_TYPES[type_name]
if type_name == tuple:
raise UnknownTypeException(f"non-parameterized argument")
raise UnknownTypeException(f"unable to parse type: {type_name}")
@classmethod
def _parse_args(cls, args, generics=None):
import re
return [cls.parse(v, generics=generics) for v in re.split(",\\s*(?![^()<>]*\\))", args)]
[docs]
@classmethod
def infer(cls, public_example: Any) -> Union["RuntimeType", str]:
"""Infer the normalized type from a public example.
:param public_example: data used to infer the type
:return: Normalized type. If the type has subtypes, returns a RuntimeType, else a str.
:rtype: Union["RuntimeType", str]
:raises UnknownTypeException: if inference fails on `public_example`
:examples:
>>> from opendp.typing import RuntimeType, L1Distance
>>> assert RuntimeType.infer(23) == "i32"
>>> assert RuntimeType.infer(12.) == "f64"
>>> assert RuntimeType.infer(["A", "B"]) == "Vec<String>"
>>> assert RuntimeType.infer((12., True, "A")) == "(f64, bool,String)" # eq doesn't care about whitespace
"""
if type(public_example) in ELEMENTARY_TYPES:
return ELEMENTARY_TYPES[type(public_example)]
if isinstance(public_example, tuple):
return RuntimeType('Tuple', list(map(cls.infer, public_example)))
if isinstance(public_example, list):
return RuntimeType('Vec', [
cls.infer(public_example[0]) if public_example else UnknownType(
"cannot infer atomic type of empty list")
])
if isinstance(public_example, dict):
return RuntimeType('HashMap', [
cls.infer(next(iter(public_example.keys()))),
cls.infer(next(iter(public_example.values())))
])
if public_example is None:
return RuntimeType('Option', [UnknownType("Constructed Option from a None variant")])
raise UnknownTypeException(public_example)
[docs]
@classmethod
def parse_or_infer(
cls,
type_name: RuntimeTypeDescriptor = None,
public_example: Any = None,
generics: List[str] = None
) -> Union["RuntimeType", str]:
"""If type_name is supplied, normalize it. Otherwise, infer the normalized type from a public example.
:param type_name: type specifier. See RuntimeType.parse for documentation on valid inputs
:param public_example: data used to infer the type
:return: Normalized type. If the type has subtypes, returns a RuntimeType, else a str.
:rtype: Union["RuntimeType", str]
:param generics: For internal use. List of type names to consider generic when parsing.
:type: List[str]
:raises ValueError: if `type_name` fails to parse
:raises UnknownTypeException: if inference fails on `public_example` or no args are supplied
"""
if type_name is not None:
return cls.parse(type_name, generics)
if public_example is not None:
return cls.infer(public_example)
raise UnknownTypeException("either type_name or public_example must be passed")
[docs]
@classmethod
def assert_is_similar(cls, expected, inferred):
"""Assert that `inferred` is a member of the same equivalence class as `parsed`.
:param expected: the type that the data will be converted to
:param inferred: the type inferred from data
:raises AssertionError: if `expected` type differs significantly from `inferred` type
"""
ERROR_URL_298 = "https://github.com/opendp/opendp/discussions/298"
if isinstance(inferred, UnknownType):
return
if isinstance(expected, str) and isinstance(inferred, str):
if inferred in ATOM_EQUIVALENCE_CLASSES:
assert expected in ATOM_EQUIVALENCE_CLASSES[inferred], \
f"inferred type is {inferred}, expected {expected}. See {ERROR_URL_298}"
else:
assert expected == inferred, \
f"inferred type is {inferred}, expected {expected}. See {ERROR_URL_298}"
elif isinstance(expected, RuntimeType) and isinstance(inferred, RuntimeType):
# allow extra flexibility around options, as the inferred type of an Option::<T>::Some will just be T
if expected.origin == "Option" and inferred.origin != "Option":
expected = expected.args[0]
assert expected.origin == inferred.origin, \
f"inferred type is {inferred.origin}, expected {expected.origin}. See {ERROR_URL_298}"
assert len(expected.args) == len(inferred.args), \
f"inferred type has {len(inferred.args)} arg(s), expected {len(expected.args)} arg(s). See {ERROR_URL_298}"
for (arg_par, arg_inf) in zip(expected.args, inferred.args):
RuntimeType.assert_is_similar(arg_par, arg_inf)
else:
# inferred type differs in structure
raise AssertionError(f"inferred type is {inferred}, expected {expected}. See {ERROR_URL_298}")
[docs]
def substitute(self, **kwargs):
if isinstance(self, GenericType):
return kwargs.get(self.origin, self)
if isinstance(self, RuntimeType):
return RuntimeType(self.origin, self.args and [RuntimeType.substitute(arg, **kwargs) for arg in self.args])
return self
[docs]
class GenericType(RuntimeType):
def __str__(self):
raise UnknownTypeException(f"attempted to create a type_name with an unknown generic: {self.origin}")
[docs]
class UnknownType(RuntimeType):
"""Indicator for a type that cannot be inferred. Typically the atomic type of an empty list.
RuntimeTypes containing UnknownType cannot be used in FFI, but still pass RuntimeType.assert_is_similar
"""
def __init__(self, reason):
self.origin = None
self.args = []
self.reason = reason
def __str__(self):
raise UnknownTypeException(f"attempted to create a type_name with an unknown type: {self.reason}")
[docs]
class DatasetMetric(RuntimeType):
"""All dataset metric RuntimeTypes inherit from DatasetMetric.
Provides static type checking in user-code for dataset metrics.
"""
pass
SubstituteDistance = DatasetMetric('SubstituteDistance')
SymmetricDistance = DatasetMetric('SymmetricDistance')
[docs]
class SensitivityMetric(RuntimeType):
"""All sensitivity RuntimeTypes inherit from SensitivityMetric.
Provides static type checking in user-code for sensitivity metrics and a getitem interface like stdlib typing.
"""
def __getitem__(self, associated_type):
return SensitivityMetric(self.origin, [self.parse(type_name=associated_type)])
AbsoluteDistance = SensitivityMetric('AbsoluteDistance')
L1Distance = SensitivityMetric('L1Distance')
L2Distance = SensitivityMetric('L2Distance')
[docs]
class PrivacyMeasure(RuntimeType):
"""All measure RuntimeTypes inherit from PrivacyMeasure.
Provides static type checking in user-code for privacy measures and a getitem interface like stdlib typing.
"""
def __getitem__(self, associated_type):
return PrivacyMeasure(self.origin, [self.parse(type_name=associated_type)])
MaxDivergence = PrivacyMeasure('MaxDivergence')
SmoothedMaxDivergence = PrivacyMeasure('SmoothedMaxDivergence')
[docs]
class Domain(RuntimeType):
def __getitem__(self, subdomain):
return Domain(self.origin, [self.parse(type_name=subdomain)])
AllDomain = Domain('AllDomain')
BoundedDomain = Domain('BoundedDomain')
VectorDomain = Domain('VectorDomain')
OptionNullDomain = Domain('OptionNullDomain')
InherentNullDomain = Domain('InherentNullDomain')
SizedDomain = Domain('SizedDomain')
[docs]
def get_domain_atom(domain):
while isinstance(domain, RuntimeType):
if isinstance(domain, (UnknownType, GenericType)):
return
domain = domain.args[0]
return domain
[docs]
def get_domain_atom_or_infer(domain: RuntimeType, example):
return get_domain_atom(domain) or RuntimeType.infer(example)
[docs]
def get_first(value):
return value[0]
